Big Data-driven Decision-Making Processes, Real-Time Advanced Analytics, and Cyber-Physical Production Networks in Industry 4.0-based Manufacturing Systems.

• Author: Rogers, Sarah

1. Introduction

   Technological breakthroughs shape socio-economic systems in terms of extensiveness, inconstancy, and complementarity across cyber-physical system-based smart factories. (Martinelli et al., 2021) Internet of Things-based decision support systems are pivotal in value creation (Adams et al., 2021; Crisan-Mitra et al., 2020; Konhausner et al., 2021; Nica et al., 2018), reconfiguring the manner companies interact and conduct business by use of artificial intelligence-based decision-making algorithms. (Awan et al., 2021)

2. Conceptual Framework and Literature Review

   Identifying digital opportunities as regards product design and manufacturing operations necessitate networking throughout the innovation process to collect industry-specific data. (Ricci et al., 2021) Internet of Things sensing networks and Internet of Things-based real-time production logistics are largely implemented amongst supply chain firms. (Hopkins, 2021) Resource circularity, maximizing earnings from green items, fashioning operations for stock, and energy efficiency constitute chief criteria in sustainable manufacturing Internet of Things. (Kumar et al., 2021) The collected data can be disjointed and challenging to acquire for external partners to handle and inspect because of the dynamic character of sustainable Industry 4.0. (Chen et al., 2021) A smart production planning and control system harnesses Internet of Things sensing networks, industrial big data analytics, and machine learning algorithms operating on the cloud or on edge devices to optimize performance by deploying heterogeneous data sources from the manufacturing system (Andronie et al., 2021a, b, c, d; Ginevicius et al., 2021; Kovacova and Lazaroiu, 2021; Poliak et al., 2021), leveraging deep learning-assisted smart process planning, and artificial intelligence-based decision-making algorithms. (Oluyisola et al., 2021) Big data analytics can upgrade business operations by inspecting customer behavior (Andrei et al., 2016; Dusmanescu et al., 2016; Kovacova et al., 2018; Nica et al., 2020), covering supply chain, logistics, and inventory management. (Maheshwari et al., 2021)

3. Methodology and Empirical Analysis

   We inspected, used, and replicated survey data from BDV, Capgemini, Deloitte, McKinsey, MHI, and Siemens, performing analyses and making estimates regarding how a smart production planning and control system harnesses Internet of Things sensing networks and Internet of Things-based real-time production logistics by use of industrial big data analytics and machine learning algorithms. Descriptive statistics of compiled data from the completed surveys were calculated when appropriate.

4. Study Design, Survey Methods, and Materials

   The interviews were conducted online and data were weighted by five variables (age, race/ethnicity, gender, education, and geographic region) using the Census Bureau's American Community Survey to reflect reliably and accurately the demographic composition of the United States.

   Data sources: BDV, Capgemini, Deloitte, McKinsey, MHI, and Siemens. Study participants: 5,400 individuals provided an informed e-consent.

   All data were interrogated by employing graphical and numeric exploratory data analysis methods. Multivariate analyses, and not univariate associations with outcomes, are more likely to factor out confounding covariates and more precisely determine the relative significance of individual variables. Results are estimates and commonly are dissimilar within a narrow range around the actual value. The data was weighted in a multistep process that accounts for multiple stages of sampling and nonresponse that occur at different points in the survey process.

   Test data was populated and analyzed in SPSS to ensure the logic and randomizations were working as intended before launching the survey. To ensure high-quality data, data quality checks were performed to identify any respondents showing clear patterns of satisficing (e.g., checking for high rates of leaving questions blank). Sampling errors and test of statistical significance take into account the effect of weighting. Question wording and practical difficulties in conducting surveys can introduce error or bias into the findings of opinion polls. The sample weighting was accomplished using an iterative proportional fitting process that simultaneously balanced the distributions of all variables. Stratified sampling methods were used and weights were trimmed not to exceed 3. Average margins of error, at the 95% confidence level, are +/-2%. The design effect for the survey was 1.3. For tabulation purposes, percentage points are rounded to the nearest whole number. The cumulative response rate accounting for non-response to the recruitment surveys and attrition is 2.5%. The break-off rate among individuals who logged onto the survey and completed at least one item is 0.2%.

   The precision of the online polls was measured using a Bayesian credibility interval. Confirmatory factor analysis was employed to test for the reliability and validity of measurement instruments. Addressing a significant knowledge gap in the literature, the research has complied with stringent methodology, reporting, and data analysis requirements.

   Flow diagram of study procedures

5. Statistical Analysis

   This survey employs statistical weighting procedures to clarify deviations in the survey sample from known population features, which is instrumental in correcting for differential survey participation and random variation in samples. Independent t-tests for continuous variables or chi-square tests for categorical variables were employed. Descriptive analyses (mean and standard deviations for continuous variables and counts and percentages for categorical variables) were used. Descriptive statistical analysis and multivariate inferential tests were undertaken for the survey responses and for the purpose of variable reduction in regression modeling.

   Mean and standard deviation, t-test, exploratory factor analysis, and data normality were inspected using SPSS. To ensure reliability and accuracy of data, participants undergo a rigorous verification process and incoming data goes through a sequence of steps and multiple quality checks. Descriptive and inferential statistics provide a summary of the responses and comparisons among subgroups. AMOS-SEM analyzed the full measurement model and structural model.

   An Internet-based survey software program was utilized for the delivery and collection of responses. Panel research represents a swift method for gathering data recurrently, drawing a sample from a pre-recruited set of respondents. Behavioral datasets have been collected, entered into a spreadsheet, and cutting-edge computational techniques and empirical strategies have been harnessed for analysis. Groundbreaking computing systems and databases enable data gathering and processing, extracting meaning through robust deployment. Non-response bias and common method bias, composite reliability, and construct validity were assessed.

   Flow diagram of statistical parameters and reproducibility

6. Results and Discussion

   Scalability and...